Flegga Mark B, Hellander Stefan, Erban Radek
School of Mathematical Sciences, Monash University, Wellington road, Clayton, Melbourne, Australia;
Department of Computer Science, University of California, Santa Barbara, CA 93106-5070 Santa Barbara, USA;
J Comput Phys. 2015 May 15;289:1-17. doi: 10.1016/j.jcp.2015.01.030.
In this paper, three multiscale methods for coupling of mesoscopic (compartment-based) and microscopic (molecular-based) stochastic reaction-diffusion simulations are investigated. Two of the three methods that will be discussed in detail have been previously reported in the literature; the two-regime method (TRM) and the compartment-placement method (CPM). The third method that is introduced and analysed in this paper is called the ghost cell method (GCM), since it works by constructing a "ghost cell" in which molecules can disappear and jump into the compartment-based simulation. Presented is a comparison of sources of error. The convergent properties of this error are studied as the time step Δ (for updating the molecular-based part of the model) approaches zero. It is found that the error behaviour depends on another fundamental computational parameter , the compartment size in the mesoscopic part of the model. Two important limiting cases, which appear in applications, are considered: (i) Δ → 0 and is fixed; (ii) Δ → 0 and → 0 such that √Δ/ is fixed. The error for previously developed approaches (the TRM and CPM) converges to zero only in the limiting case (ii), but not in case (i). It is shown that the error of the GCM converges in the limiting case (i). Thus the GCM is superior to previous coupling techniques if the mesoscopic description is much coarser than the microscopic part of the model.
本文研究了三种用于耦合介观(基于区室)和微观(基于分子)随机反应扩散模拟的多尺度方法。将详细讨论的三种方法中的两种此前已在文献中报道;双机制方法(TRM)和区室放置方法(CPM)。本文介绍并分析的第三种方法称为虚拟单元法(GCM),因为它通过构建一个“虚拟单元”来工作,分子可以在其中消失并跳入基于区室的模拟中。给出了误差来源的比较。研究了随着时间步长Δ(用于更新模型的基于分子的部分)趋近于零,该误差的收敛特性。发现误差行为取决于另一个基本计算参数,即模型介观部分的区室大小。考虑了应用中出现的两种重要极限情况:(i)Δ→0且固定;(ii)Δ→0且→0,使得√Δ/固定。先前开发的方法(TRM和CPM)的误差仅在极限情况(ii)下收敛到零,而在情况(i)下不收敛。结果表明,GCM的误差在极限情况(i)下收敛。因此,如果介观描述比模型的微观部分粗糙得多,GCM优于先前的耦合技术。